Past work on Human-AI teaming has focused on understanding how humans form mental models (and related constructs like situational awareness) about AI [1]}, [2]}. For example, effective Human-AI interaction has been shown to rely critically on humans ability to understand or anticipate what AI agents are doing [3]}, [4]}, [5]}, [6]}, [7]}. Significantly less work has focused on how AI can form “mental models” of humans. [8]} highlight this disparity by identifying situations where humans having mental models of the AI is not helpful, while AI having “mental models” of humans is. Given the challenges of designing multi-agent systems, human-AI teaming work has often focused on studying pairs of one agent and one human rather than multi-person teams , , [9]}, , .
Finally, past work has often side-stepped challenges posed by language-based communication
by constraining the scope to spatial or highly stylized tasks [10]}, [11]}, [12]}. Others use Wizard of Oz techniques [13]}, [14]} to facilitate communication-based human-AI teaming interaction. To build an autonomous AI teammate that improves human-only team performance, one must build agents that overcomes these obstacles. This becomes more challenging in a team of unfamiliar teammates, without a priori knowledge, while learning dynamically from language-based communication [15]}.
